There is no shortage of opinions on the potential for AI technologies in business. However, the current round of solutions is often viewed as expensive, proprietary, and complex to deploy and manage. When will AI solutions scale industry-wide? Is it possible
to measure ROI for automation? How does AI rank against other corporate initiatives? The state of AI technology and its future is spoken here, from development neuromorphic chipsets to democratizing deep learnin toolsets next wave machine vision,
emotion, gesture, NL, new algorithms, HPC, quantum computing will all be shared by the industry's best and brightest.

Combining artificial intelligence with the human workforce will deliver a highly efficient contact center, but how do you design an experience that leads to reduced agent churn and high customer satisfaction? Jessica Langdorf will share her expertise
on designing AI powered customer engagement solutions for customers and contact center agents.

The enterprise and end users need a way to explain why the AI made a prediction. Whether declining a loan or mortgage application, settling an insurance claim, or recommending a personalized medical plan to maintain optimal health, industry watchdogs
and regulators are reluctant to embrace intelligent systems without some explanation of how the data input generated the machine output. Some technology providers have claimed that they are already delivering explainable AI systems, but
these are few and far between.

Discuss what is meant by explainable AI and what is it that businesses and industry regulators want to know about predictions

Understand the trade-off between AI transparency and performance along with the implications for intellectual property

What is the current state of the technology in delivering truly explainable AI systems

As narrow AI implementation scales to address complex business judgments and AGI, does the demand for explainable AI increase beyond finance, healthcare, and legal vertical markets?

Machine learning is currently viewed as a single tool. However, ML is not a static environment. Researchers have already developed advanced technology to evolve ML to process larger amounts of data even faster. Some developers for example are
examining how ML can incorporate blockchain for safety and security within the ML model. ML in its various forms are being integrated into and with other highly advanced intelligent systems such as NLP, image processing, etc. for multitudes
of applications. This panel of AI and data science researchers is pushing the bleeding edge of emerging technology and identifying the future of ML.

What are the opportunities for evolving ML in the enterprise?

How long can the current state of the technology evolve before we seen the next quantum leap?

What are the risks of market fragmentation and adoption as ML becomes more than one thing?

How can other emerging technologies, such as quantum computing and blockchain, be combined with machine learning to create the next frontier in data science?

The race for making perfect hardware to accelerate artificial intelligence (AI) applications is heating up and many companies are jumping in with their products and solutions. Of the three key parts of hardware infrastructure – compute,
networking, and storage – compute has made significant progress in the last couple of years. The other two areas, storage and networking, are lagging and have yet to see major innovations pertaining to AI applications. In the next few
years, however, more research and development (R&D) will go into these areas and new products will emerge that are designed specifically for AI.

What are the market drivers and milestones that will enable AI-driven hardware to grow to $115.4 billion by 2025?

Which hardware segments will account for the majority of sales?

What impact will FPGAs, ASICs, SoC accelerators, and other emerging chipsets have on the current dominance of GPUs and CPUs?